Digital platforms and the creator economy
How platforms, recommendation algorithms, sponsorship and peer recognition shape what creators produce and what users consume.
research
Three lines of workResearch
I am a Senior Lecturer (Associate Professor) in Business Economics at the University of Edinburgh Business School and a Fellow of the University's Generative AI Laboratory. I received my PhD in Economics from the Toulouse School of Economics. My research combines economic theory, empirical methods and machine learning to study how digital markets work.
Fintech, blockchain and contracts
Information and ratings in crypto-asset and peer-to-peer lending markets, smart contracts versus courts, and bank lending relationships.
The economics of AI
Open models and data feedback, AI coding agents in open-source communities, compute constraints, and machine-learning methods for causal inference.
recent
Talks, grants and serviceRecent
- Jul. 2026teaching“Intelligent Economics”, International Summer School, Renmin University of China
- Jun. 2026service“Economics of Fintech Conference”, Scientific Committee
- May 2026talk“Tokenisation Beyond Technology”, Invited Speaker, School of Informatics, Edinburgh Tokenisation Summit
- May 2026talk“Artificial Intelligence and Economics: Productivity, market design, and governance”, Invited Speaker, Károli Gáspár University
- Jan 2026grantNew grant: Mapping Online Behaviour to Beliefs: A Machine Learning Approach (Edinburgh Future Institute Internal Fund, Principal Investigator)
- Oct 2025grantNew grant: Breaking the Echo Chamber: Using Algorithms to Reduce Online Conflict (ESRC Impact Acceleration Account Fund, Principal Investigator)
selected publications
In journalsSelected publications
- Sanxi Li, Hailin Sun, Tong Wang, and Jun Yu. “Assortative matching and risk sharing.” Journal of Economic Theory 163 (2016): 248–275.
Abstract
This paper explores the sorting patterns in a two-sided matching market where agents facing different risks match to share them. When preference belongs to the class of harmonic absolute risk aversion (HARA), the risk premium is perfectly transferable within each partnership; thus a stable match minimizes the social cost of risk. In the systematic risk model, where agents are ranked by their holdings of a common risky asset, the convexity of the joint risk premium in joint risk size leads to negative assortative matching (NAM). In the idiosyncratic risk model, where agents are ranked by their independent riskiness in the sense of second-order stochastic dominance (SSD), NAM arises when preference exhibits decreasing absolute risk aversion (DARA) in the sense of Ross and riskier background risk leads to more risk-averse behavior. However, NAM may fail to arise when riskier background risk leads to more risk-tolerant behavior.
- Zonglai Kou, Patrick Rey, and Tong Wang. “Non-Obviousness and Screening.” The Journal of Industrial Economics 61, no. 3 (2013): 700–732.
Abstract
The paper offers a novel justification for the non-obviousness patentability requirement. An innovation involves two stages: research results in a technology blueprint, which development transforms into a profitable activity. An innovator, who is either efficient or inefficient, must rely on outside finance for the development. Only patented technologies are developed. Strengthening the non-obviousness requirement alleviates adverse selection by discouraging inefficient innovators from doing research, but creates inefficiencies by excluding marginal innovations. We show that it is socially optimal to raise the non-obviousness requirement so as to exclude bad innovators; we also provide several robustness checks and discuss the policy implications.
- Xintong Han, Zimin Liu, and Tong Wang. “Nonlinear pricing in multidimensional context: An empirical analysis of energy consumption.” International Journal of Industrial Organization 91 (2023): 103034.
Abstract
Modern business practices frequently employ a blend of pricing strategies to segment markets effectively. As a result, consumers may encounter pricing schedules that are nonlinear and multidimensional. This paper presents a structural approach for estimating multidimensional nonlinear pricing models involving multiple decision variables in an energy market. Using a unique, rich panel dataset of Chinese household electricity consumption, we structurally estimate consumer preferences under the influence of an Increasing Block Price (IBP) and a Time-of-Use (ToU) system. Our structural approach allows us to distinguish and evaluate household-level price elasticities of demand, presenting a novel explanation for consumer's feedback on marginal price changes. Through model-based simulations, we demonstrate that a 1% increase in price corresponds to a 0.7% reduction in total electricity demand. However, our analysis indicates that practical opportunities for optimization within multi-dimensional pricing systems are limited. Our findings offer distinct insights into the complex interplay between intricate pricing structures and energy consumption behavior, thereby providing valuable guidance for policymakers and regulators.
- Xintong Han, Yushen Li, and Tong Wang. “Peer recognition, badge policies, and content contribution: An empirical study.” Journal of Economic Behavior & Organization 214 (2023): 691–707.